This project's general objective is to provide better psychometric methods in epidemiological studies. The particular aims are to develop new psychometrics and determine their applicability in alcohol and depression studies applied to large-scale national samples, such as the 1988 National Health Interview Survey of about 50,000 observations. Of special interest is the multivariate analysis of sets of symptom items administered to enable diagnoses of various disorders such as alcohol dependence, alcohol abuse, and major depressive disorder. The project will attempt to formulate appropriate statistical models for describing measurement errors in such diagnoses and propose improved ways for relating such disorders to hypothesized causes and consequences. The statistical approaches of the project draw on the general framework of factor analysis and structural equation modeling of systems of relationships among latent variable constructs. In particular, the project will utilize recent advances related to categorical and other non-normal measurements, such as operationalized in the Principal Investigator's LISCOMP computer program. Three method areas will be studied: Regression analysis with error-prone dichotomous dependent variables, factor analysis of categorical and other non-normal variables, and longitudinal structural modeling. Key research issues are the proper latent variable modeling of various conceptualizations of disorders related to alcohol use, including modeling with dichotomous indicators of continuous traits, latent classes, and mixtures thereof. The choice of proper latent variable distributions will be studied, as well as classification issues related to diagnoses, including comparisons between clinical and psychometric approaches. The proper modeling of hypothesized causal relationships will be studied in new types of structural models, including multivariate structural probit modeling of change over time and the covariation of alcoholism and depression.